Many software applications expect users to return over time to the application. Generally these applications engage users in work that needs to be completed over calendar time. In particular, software applications designed to help individuals change behavior incorporate a dynamic workflow that encourages a user to conduct different activities at different times. These applications can be deployed via web, cell phone or client-server interfaces.
A key challenge for tools that expect ongoing, overtime use by users (like these software applications) is managing ongoing engagement with the tool itself. Use of the tool (software application) is often a pre-requisite for the other desired actions by users.
The inability to model and predict future engagement of current users presents a problem for software application operators as they cannot adjust program features, or outreach protocols to improve return engagement. These software applications face the problem that they cannot optimize the user experience to maximize the likelihood of return and thus cannot maximize program success.
Current technologies do not provide appropriate tools to model and predict the likelihood that a user will return to the application in the future. These technologies rely on basic regression models, correlation analysis, or appeal to best practices or user surveys to gather information on the determinants of repeat use. These solutions do not fully utilize the existing data on actual user engagement to form individualized models able to provide prediction of future return.
Accordingly, there is a need by software application developers and operators for predicting the likelihood of continued engagement with a software application.